# Human Activity Recognition Based on Wearable Sensor Data: A Standardization of the State-of-the-Art

arXiv: https://arxiv.org/abs/1806.05226 · HTML: https://mlsanity.com/paper/1806.05226/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 14 of 14)

- [Review on Action Recognition for Accident Detection in Smart City Transportation Systems](https://mlsanity.com/paper/2208.09588/) (2022)
- [Human Activity Recognition using Wearable Sensors: Review, Challenges, Evaluation Benchmark](https://mlsanity.com/paper/2101.01665/) (2021)
- [Role of Data Augmentation Strategies in Knowledge Distillation for Wearable Sensor Data](https://mlsanity.com/paper/2201.00111/) (2022)
- [P2LHAP:Wearable sensor-based human activity recognition, segmentation and forecast through Patch-to-Label Seq2Seq Transformer](https://mlsanity.com/paper/2403.08214/) (2024)
- [Too Good To Be True: performance overestimation in (re)current practices for Human Activity Recognition](https://mlsanity.com/paper/2310.11950/) (2023)
- [Standardizing Your Training Process for Human Activity Recognition Models: A Comprehensive Review in the Tunable Factors](https://mlsanity.com/paper/2401.05477/) (2024)
- [WaveGlove: Transformer-based hand gesture recognition using multiple inertial sensors](https://mlsanity.com/paper/2105.01753/) (2021)
- [LSTM-CNN: An efficient diagnostic network for Parkinson's disease utilizing dynamic handwriting analysis](https://mlsanity.com/paper/2311.11756/) (2023)
- [Explainable Deep Learning Framework for Human Activity Recognition](https://mlsanity.com/paper/2408.11552/) (2024)
- [Am I fit for this physical activity? Neural embedding of physical conditioning from inertial sensors](https://mlsanity.com/paper/2103.12095/) (2021)
- [Automated Mobility Context Detection with Inertial Signals](https://mlsanity.com/paper/2205.08409/) (2022)
- [The Potential of Wearable Sensors for Assessing Patient Acuity in Intensive Care Unit (ICU)](https://mlsanity.com/paper/2311.02251/) (2023)
- [WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition](https://mlsanity.com/paper/2606.13194/) (2026)
- [When 10,000 Windows Are Not 10,000 Tests: Auditing Statistical Confidence in Sliding-Window Time-Series Classification](https://mlsanity.com/paper/2609.30721/) (2026)

## Abstract (arXiv)

Human activity recognition based on wearable sensor data has been an attractive research topic due to its application in areas such as healthcare and smart environments. In this context, many works have presented remarkable results using accelerometer, gyroscope and magnetometer data to represent the activities categories. However, current studies do not consider important issues that lead to skewed results, making it hard to assess the quality of sensor-based human activity recognition and preventing a direct comparison of previous works. These issues include the samples generation processes and the validation protocols used. We emphasize that in other research areas, such as image classification and object detection, these issues are already well-defined, which brings more efforts towards the application. Inspired by this, we conduct an extensive set of experiments that analyze different sample generation processes and validation protocols to indicate the vulnerable points in human activity recognition based on wearable sensor data. For this purpose, we implement and evaluate several top-performance methods, ranging from handcrafted-based approaches to convolutional neural networks. According to our study, most of the experimental evaluations that are currently employed are not adequate to perform the activity recognition in the context of wearable sensor data, in which the recognition accuracy drops considerably when compared to an appropriate evaluation approach. To the best of our knowledge, this is the first study that tackles essential issues that compromise the understanding of the performance in human activity recognition based on wearable sensor data.